The invention lies in the field of Ultra Wide Band (UWB) impulse radio technology, that is to say using signals for which the ratio between the width of the band at 10 dB and the central frequency is greater than 25% and relates more particularly to the correction of the clock drift between a transmitter and a UWB receiver during integration.
This radio technology forms the subject of IEEE standard 802.15.4a.
The applications of this technology are of the personal network and low-speed local wireless networks type, namely: communicating tags, networks of sensors, ADHOC networks (that is to say ones capable of self-organizing with no previously established infrastructure), location of objects, security, etc.
In impulse Ultra Wide Band communication systems, the data sent between a transmitter and a receiver are electromagnetic pulses coded in amplitude, in phase, or in position [cf. “M. Z. Win and R. A. Scholtz, “Impulse radio How it works”, IEEE Comm. Letters, vol. 2, no. 2, pp. 36-38, February 1998”). A critical step of the process of communication between the transmitter and the receiver occurs on reception of the data packets, to determine the instants at which these data packets arrive at the receiver level. Synchronization between the signal received and the receiver is then necessary. This synchronization is all the more difficult to carry out when the medium in which the communication is performed is disturbed (appearance of multipath).
IEEE standard 802.15.4a, which proposes a physical layer of Ultra Wide Band type for low-speed personal wireless networks, imposes new constraints in terms of circuit complexity. Moreover, it proposes the use of data packets having a relatively high duration, associated with particularly strong constraints on clock imperfections. Together, these two aspects require the devising of robust schemes for detecting and correcting clock imperfections. Numerous solutions currently exist that make it possible to perform the synchronization of a receiver with a data packet. A frequently used technique consists of correlating the signal received with a waveform at various instants (cf. patent applications WO 1996-041432, WO 2001-073712, WO 2001-093442, WO 2001-093444, WO 2001-093446, US 2005-0089083 and US 2006-0018369).
The proposed architectures allow very fast synchronization but at the price, however, of very high circuit complexity (the radiofrequency components used are mixers, integrators, local oscillators, etc.).
But this complexity and the cost overhead is not acceptable for certain applications, in particular for location applications, for example for firemen in a smoke-filled environment or for locating avalanche victims (see for example WO2006/003294), therefore a problem arises in respect of the drift of the clocks between the transmitter and the receiver, which will be illustrated in greater detail in what follows.
Indeed, to locate a first transmitter/receiver with respect to a second transmitter/receiver, use is generally made of the measurement of the flight time—or a quantity tied to the flight time—of an electromagnetic pulse of very short duration.
This principle, also called “two ways ranging” (TWR), is illustrated in FIGS. 1 and 2.
FIG. 1 shows a first 1 and a second 2 transmitter/receiver 1, each having its own clock respectively H1 and H2. These clocks are not synchronized.
FIG. 2 shows a timechart of transmission and reception of the signals exchanged between the first 1 and the second 2 transmitter/receiver.
In this exchange of signals, the transmitter/receiver 1 transmits an electromagnetic pulse at the instant t0. Having regard to the velocity of propagation of light c, this pulse will reach the transmitter/receiver 2 at the time t=t0+d/c, where d represents the distance between the transmitter 1 and the receiver 2.
At this juncture, the device 2 could measure the distance d if it knew the precise transmission time t0, but this is not the case since the two devices may not be synchronized precisely because of their clock drifts.
Therefore, after a known duration ΔT, the transmitter/receiver 2 transmits in its turn a pulse that arrives at the level of the device 1 at the time t=t0+2d/c+ΔT.
In contrast to the transmitter/receiver 2, the transmitter/receiver 1 can actually evaluate the distance, since it knows the instant t0 and the duration ΔT, which is a constant of the system.
The precision with which the times of arrival and hence the distances may be measured is directly related to the duration of the pulse. Typically, the durations of these pulses are on the scale of a nanosecond (during 1 nanosecond, the pulse travels 30 cm). It is moreover known that a pulse that is very narrow in the time domain is very spread in the frequency domain. For this reason radio systems using short pulses are termed Ultra Wide Band (UWB) radio, with reference to their spectral spread.
Because of their large spectral spread (several hundreds of megahertz), these systems are also characterized by a very weak transmission level. Indeed, there is no frequency band allocated exclusively for UWB systems, the latter must therefore use bands reserved for other “conventional” radio systems without, however, disturbing them.
The legislators have therefore fixed standards regarding very low transmission powers (−41.3 dBm/MHz between 3 and 5 GHz for example) to forestall any interference from the UWB system to the “conventional” radio system. Conversely, UWB systems encounter the transmissions of conventional radio systems head on.
The low transmission power on the one hand and the presence of much interference on the other hand imply that UWB systems are limited to applications at low range (typically a few meters). For example, measurements have shown that the range of a pulse in the 500 MHz-1 GHz band of 3 V amplitude was about 3 meters (for isotropic antennas).
However, certain application scenarios, especially location, require much greater ranges, on the order of several tens of meters, for example.
To address the concern not to increase radiation power, increased range may be achieved only through digital processing operations that are all akin to an averaging mechanism.
Indeed, in order to detect a signal, its amplitude must be greater than that of the noise. In practice, the amplitude of the signal should be at least 3 times greater than the standard deviation of the noise, that is to say the signal-to-noise ratio should be at least about 10 dB.
The most effective technique making it possible to reduce noise consists, in principle, in transmitting the same pulse several times at the level of the transmitter and in calculating the average (or the sum) of the signals received at the level of the receiver.
The period T with which a pulse 4 is repeated is called the PRP (Pulse Repetition Period). The receiver will thereafter “chop” the signal received according to this same period before performing the summation as indicated in FIG. 3. This mechanism is also called “integration”. The integrated signal 5 is seen schematically in FIG. 3.
The principle underlying the integration mechanism is that the “signal” is summed better than the “noise”. To understand this principle, it is necessary firstly to represent a sample digitized by the receiver r(t) as the sum of a “useful signal” (the pulse) s(t) and of noise b(t)
[1]r(t)=s(t)+b(t)  (1)
Because noise is a random variable, its amplitude is characterized by its standard deviation σ, which is also defined as the square root of its variance.
The signal-to-noise ratio of the sample r(t) is then given by
      [    2    ]                                            SNR            ⁡                          (              t              )                                =                      20            ⁢                          log              ⁡                              (                                                      s                    ⁡                                          (                      t                      )                                                        σ                                )                                                                          (          2          )                    
The integrated signal rm(t) is described by
      [    3    ]                                                          r              m                        ⁡                          (              t              )                                =                                    ∑                              i                =                0                                            n                -                1                                      ⁢                          r              ⁡                              (                                  t                  +                  iT                                )                                                                          (          3          )                    
In a trivial manner, when an identical signal is added together n times, its amplitude is multiplied by n
[4]sm(t)=s(t)+s(t+T)+s(t+2T)+ . . . s(t+(n−1)T)=n·s(t)  (4)
On the other hand, adding a random variable together n times amounts to multiplying its variance by n and therefore its standard deviation by √n
[5]σm2=σ2+σ2+σ2+ . . . σ2=n·σ2  (5)Therefore[6]σm=√{square root over (n)}·σ2  (6)
The signal-to-noise ratio of the integrated signal is therefore improved by a factor 10·log(n) with respect to the signal-to-noise ratio of the non-integrated signal.
      [    7    ]                                                          SNR              m                        ⁡                          (              t              )                                =                                    20              ·                              log                ⁡                                  (                                                            n                      ⁢                                                                                          ·                                              s                        ⁡                                                  (                          t                          )                                                                                                                                    n                                            ⁢                      σ                                                        )                                                      =                                          10                ·                                  log                  ⁡                                      (                    n                    )                                                              +                              SNR                ⁡                                  (                  t                  )                                                                                          (          7          )                    
In order to spread the transmission spectrum, to convey an item of information or else to distinguish the pulses, the signal sent is not simply periodic but is modulated according to a particular coding. For example, it is possible to modify the position τ(i) of the pulse in window number i as indicated in FIG. 4.
In this case, the chopping and averaging mechanism described previously would not operate since the pulse is not always “at the same place” in the window of the PRP.
More generally, the received signal can be written:
      [    8    ]                                            r            ⁡                          (              t              )                                =                                                    ∑                                  i                  =                  0                                                  n                  -                  1                                            ⁢                                                δ                  ⁡                                      (                                          t                      +                      iT                      +                                              τ                        i                                                              )                                                  ·                                  A                  i                                ·                                  p                  ⁡                                      (                    t                    )                                                                        +                          b              ⁡                              (                t                )                                                                          (          8          )                    
With                [τ0 . . . τn-1]=a code of positions with 0<τi<T        [A0 . . . An-1]=a polarity code with Ai=±1        p(t)=a signal describing the fundamental pulse received        b(t)=white noise        
To perform the reception of a coded signal, the signal received is correlated with the expected sequence α(t)
      [    9    ]                                            α            ⁡                          (              t              )                                =                                    ∑                              i                =                o                                            n                -                1                                      ⁢                                          δ                ⁡                                  (                                      t                    +                    iT                    +                                          τ                      i                                                        )                                            ·                              A                i                                                                          (          9          )                    
From this point of view, the simple summation as illustrated in FIG. 3 may be interpreted as the particular case of a correlation with a Dirac comb.
We obtain the integrated signal
      [    10    ]                                                          r              m                        ⁡                          (              t              )                                =                                    ∫                              -                ∞                            ∞                        ⁢                                                            r                  ⁡                                      (                    u                    )                                                  ·                                  a                  ⁡                                      (                                          u                      -                      t                                        )                                                              ⁢                                                          ⁢                              ⅆ                u                                                                          (          10          )                    
In particular
      [    11    ]                                                          r              m                        ⁡                          (              0              )                                =                                    ∑                              t                =                0                                            n                -                1                                      ⁢                                          A                                  t                  2                                            ·                              p                ⁡                                  (                  0                  )                                            ·                              +                                                      ∑                                          t                      =                      0                                                              n                      -                      1                                                        ⁢                                                            A                      t                                        ·                                                                  b                        ⁡                                                  (                                                      t                            +                            iT                            +                                                          τ                              t                                                                                )                                                                    .                                                                                                                                (          11          )                    
The signal-to-noise ratio of the integrated signal is by definition the ratio of the expectation to the standard deviation of the integrated signal:
                                              ⁢                                                                              SNR                  ⁡                                      (                    0                    )                                                  =                                  20                  ⁢                                      log                    ⁡                                          (                                                                        E                          ⁢                                                      {                                                                                          r                                m                                                            ⁡                                                              (                                0                                )                                                                                      }                                                                                                                                var                            ⁢                                                          {                                                                                                r                                  m                                                                ⁡                                                                  (                                  0                                  )                                                                                            }                                                                                                                          )                                                                                                                                              =                                  20                  ⁢                                      log                    ⁡                                          (                                                                                                                                  ∑                                                              i                                =                                0                                                                                            n                                -                                1                                                                                      ⁢                                                          A                              i                              2                                                                                                                                                                          ∑                                                                  i                                  =                                  0                                                                                                  n                                  -                                  1                                                                                            ⁢                                                                                                                                                                      A                                    i                                                                                                                                    ·                                                                  σ                                  2                                                                                                                                                                    ⁢                                                  p                          ⁡                                                      (                            0                            )                                                                                              )                                                                                                                                              =                                                      20                    ⁢                                          log                      ⁡                                              (                                                                                                            np                              ⁡                                                              (                                0                                )                                                                                      ·                                                                                                              ·                                                              n                                                                                      ⁢                            σ                                                                          )                                                                              =                                                            SNR                      0                                        +                                          10                      ⁢                                              log                        ⁡                                                  (                          n                          )                                                                                                                                                                            (        12        )            
The same integration gain is found as in the case of the simple average.
To summarize, there is no basic difference between the calculation of the average of a periodic signal or the reception of a signal coded by correlation from the point of view of the integration gain.
Throughout the following, we will restrict ourselves to the particular case of the integration of a periodic signal, bearing in mind that the same principles apply to the more general case of the reception of a signal coded by correlation.
Referring to FIG. 5, it is considered that the signal received is composed of a succession or train of N PRPs (N being a natural integer), each of the PRPs comprising K samples (K being a natural integer). A sample xnk therefore corresponds in this case to sample number k (also called the phase) of PRP number n with k lying between 0 and K−1 and n lying between 0 and N−1. The date of the sample xnk is simply
[12]4  1 3)
The integrated signal represented below of the train of N PRPs, and that represents a duration T equal to the PRP, comprises K samples numbered from 0 to K−1. The sample yk of the integrated signal is therefore simply
      [    13    ]                                            y            k                    =                                    ∑                              n                =                0                            N                        ⁢                          x              n              k                                                            (          14          )                    
The above formulas have shown that the processing gain obtained by integration increases according to a law of the form 10·log(N), where N is the number of integrations. In order to increase the range of a UWB device it is therefore appropriate to increase the number of integrations, that is to say the number of pulses sent. Precisely, it is shown that to increase the range by a factor α, the number of pulses must be multiplied by α2. In practice, to obtain large ranges, the number of pulses to be integrated quickly becomes very large. For example, having regard to the various losses of the system and the range to be reached (about 100 meters), the number of pulses to be integrated may reach 65,000.
The problem that arises during integration of a very large number of pulses is that, to remain effective, the integration mechanism must add pulses in phase. To understand what this implies, the digitized signal as illustrated in FIG. 6 may be considered. The signal received, a succession of PRPs, possesses for example a periodicity of 1 μs (clock of the transmitter) and, if it is chopped according to the same periodicity by the receiver, the samples represented below having the same phase are aligned. In this case, the summation will be performed perfectly.
On the other hand, when the number of pulses increases, the duration of integration lengthens and the clock drift between the transmitter and the receiver then becomes a very sensitive parameter. For example, if the PRP has a duration of 1 μs, the integration of 65,000 pulses therefore lasts 65000·1 μs=65 milliseconds. To preserve alignment at the level of the sample (that is to say to within 500 picoseconds), the clock drift must be less than
      [    14    ]                                            δ            <                                          500                ⁢                                                                  ⁢                ps                                            65000                ×                1                ⁢                                                                  ⁢                μs                                              =                      0.007            ⁢                                                  ⁢            ppm                                                (          15          )                    
Now, the best quartz crystals on the market have a drift of the order of 1 ppm. In particular, these drifts originate for example from a change of temperature, especially when turned on, the aging of the quartz, mechanical shocks undergone by the quartz or else variations in the quartz supply voltage.
The consequence of integration in the presence of clock drift is presented in FIG. 7. It is noted that in this case, the integrated signal is spread and the integration gain is no longer optimal.
It is therefore understood that the knowledge of the clock drift between the transmitter and the receiver is important in order to be able to increase the range of a UWB system.
A method for estimating the drift between two clocks of a UWB system based on a trellis approach, thereafter allowing coherent integration of the diverse samples received, is known from document EP 1 903 702 A1. According to this approach, partial summations are performed that must all be stored in a memory.
Next, by a multipath-based integration process (see FIG. 11 of this document), a large number of summations is carried out and thereafter compared with each other. The best is thereafter selected via an energy criterion for example.
However, the use of the trellis requires significant hardware resources and a non-negligible amount of a posteriori processing. Indeed, if we use for example 2048 points in the PRP and a maximum shift of ±128 samples for 65,535 PRPs (i.e. 128 different segments), the processing operations are composed of                construction of the partial sums: 2048×65535=134 million additions        storage of the partial sums: 2048×128=262,000 memory words        scan of the trellis: 2048×128×256=67 million additions        storage of the results of the scan=256×2048=524,000 words        search for the best scan: 524,000 words to be processed        
Moreover, the operations of scanning the trellis and of searching for the best scan are performed after complete reception of the signal, which may be problematic for real-time processing.
Finally, with the aim of limiting the necessary hardware resources, the search for the drift is not performed over the whole set of phases k but only over a limited number of them. This option provided for in the aforementioned document nonetheless assumes that the transmitter and the receiver have been synchronized previously, that is to say the receiver “knows” in which part of the PRP the pulse lies. Now, this approach is not tenable in systems with very low signal-to-noise ratios since synchronization can only be done after drift estimation. It is not possible to synchronize the signal if the latter has not been correctly integrated (poor signal-to-noise ratio) and it is not possible to integrate a signal correctly if the clock drift between the transmitter and the receiver is not known.
A method associated with a correlation device, making it possible to reduce energy consumption by turning on the receiver solely at the moment at which the pulse enters, is also known from the document “Timing tracking algorithms for impulse radio (IR) based ultra wideband (UWB) systems” (Li Huang et al. 2007 3rd International conference on wireless communications, networking, and mobile computing—WiCOM '07 21-25 Sep. 2007 Shanghai, China, 21 Sep. 2007 (2007-09-21), 25 Sep. 2007 (2007-09-25), pages 570-573, XP002662469, 2007 3rd International conference on wireless communications, networking, and mobile computing, WiCOM '07 IEEE Piscataway, N.J., USA). This method makes it necessary to synchronize the processing of the pulse with its reception. However, even if the synchronization is perfect at the start of the method, the clock drift gives rise to desynchronization between the reception and the processing of the pulse in the course of time.
In order to alleviate this desynchronization, the receiver seeks to find which part of the PRP the pulse is in. For this purpose, the method uses an architecture in which three instants of synchronization are considered in parallel.
In order to improve the signal-to-noise ratio and the performance of the approach, the method requires the insertion of “pilot pulses” into the PRP. The pilot pulses are sampled at the three instants of synchronization. The method thereafter compares the energies contained by the pilot pulses at the three instants of synchronization. The instant of synchronization selected for the processing of the pulse is the one containing the maximum energy.
However the insertion of pilot pulses increases the number of operations required and complicates the method.